Abstract
Node classification algorithms are widely used for the task of node label prediction in partially labeled graph data. In many problems, a user may wish to associate a confidence level with a prediction such that the error in the prediction is guaranteed. We propose adopting the Conformal Prediction framework [17] to obtain guaranteed error bounds in node classification problem. We show how this framework can be applied to (1) obtain predictions with guaranteed error bounds, and (2) improve the accuracy of the prediction algorithms. Our experimental results show that the Conformal Prediction framework can provide up to a 30% improvement in node classification algorithm accuracy while maintaining guaranteed error bounds on predictions.
This material is based upon work supported by the U.S. Army Research Office under grant number W911NF1810047.
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Notes
- 1.
Note that the “\(\le \)” sign in Eq. 1 changes to “\(\ge \)” if we are using a non-conformity function instead of conformity.
- 2.
Obtained from https://archive.org/download/oxford-2005-facebook-matrix.
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Wijegunawardana, P., Gera, R., Soundarajan, S. (2020). Node Classification with Bounded Error Rates. In: Barbosa, H., Gomez-Gardenes, J., Gonçalves, B., Mangioni, G., Menezes, R., Oliveira, M. (eds) Complex Networks XI. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-40943-2_3
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